With the increased availability of smart phones, tablets and laptops in mobile data-/telecommunication networks, data traffic in the mobile networks is rapidly increasing. A mobile phone is no longer used exclusively to make to phone calls and send short messages, but is utilized for instance for watching video clips and streaming media, such as audio and video. During busy hours, the network can be overloaded and thus become congested due to increased quantities of data. Therefore, it is desirable for network operators to balance the traffic load by pre-buffering user content to the smart phones during off-peak hours when the network traffic load is lighter in order for the user to subsequently render the content at a desired point in time.
Pre-buffering user content is generally based on the prediction of user consumption behaviour. Since the network has limited capacity and the mobile phone has limited storage space, it must be determined which content data items, such as audio or video items, to be pre-buffered. The current solution to this problem is to rank the items to be downloaded by an associated probability that the user will want to render a particular item and pre-buffer the highest ranked items for subsequent rendering. This ranking may be embodied in the form of a list where, e.g., ten items are sorted according to their associated rendering probability. Thereafter, a buffer of the mobile phone will be filled with a selected couple of high-ranked content data items, such that the user subsequently can render these selected items.
The probabilities used for ranking items for download are typically acquired from recommender systems, also known as recommendation systems, and are determined based on user preferences, user behaviour, social network information, event calendars, editorials, campaigns, and other types of recommendations, as is known in the art. Such recommender systems may be implemented by means of collaborative or content-based filtering, or a combination thereof, referred to as hybrid recommender systems.
However, since the accuracy of the predictions is normally quite low, the benefits from pre-buffering are small. Prior art pre-buffering methods used could in fact increase the total network traffic load and reduce mobile phone battery life, in particular when the downloaded content is represented by a large quantity of data and/or the prediction accuracy is low.